Semantic chunking splits a document at topic boundaries rather than at fixed character or token counts. Instead of slicing every 500 characters regardless of what the text is saying, it detects where one idea ends and another begins — using embedding similarity between adjacent sentences — and makes the cut there.
Why it matters
Standard chunking by character count is simple and fast, but it treats the document as a uniform stream of characters with no regard for meaning. A cut that lands mid-paragraph severs a thought; a chunk that straddles two unrelated topics produces a blurry embedding that retrieves poorly for both.
Semantic chunking solves this by making cuts where the content actually changes. Two consecutive sentences that are semantically distant — measured by a drop in vector embedding similarity — signal a topic shift, and the split happens there. The result is chunks that are coherent units of meaning: each one covers a single idea, which means its embedding accurately represents what it says rather than averaging across two unrelated topics.
That coherence pays off at retrieval time. A query about "indemnification limits" should surface the chunk that contains only that clause, not a mixed chunk that happens to include it alongside boilerplate about notices. Self-contained chunks match queries more precisely, and more precise matches make retrieval-augmented generation more reliable — grounding answers in the passage that actually contains the answer, rather than a passage that is merely adjacent to it.